Sparse Stochastic Online AUC Optimization for Imbalanced Streaming Data

Abstract

Area Under the ROC Curve (AUC) is an objective indicator of evaluating classification performance for imbalanced data. In order to deal with large-scale imbalanced streaming data, especially high-dimensional sparse data, this paper proposes a Sparse Stochastic Online AUC Optimization (SSOAO) method. Specifically, we first turn the standard online AUC optimization problem into a stochastic saddle point problem, then optimizing AUC by solving stochastic saddle point problem through AdaGrad optimizer. A sparse regularization term is also added for learning sparse data with high dimension. Comprehensive evaluation has been carried out on the recent benchmark. The experimental results show that the proposed SSOAO has the comparable performance on low-dimensional data, and outperforms other popular AUC optimization methods on high-dimensional sparse imbalanced streaming data. Both time and space complexity for model updating are reduced from \(O(d^2)\) to O(d), which equal to the data dimension.